Infrastructure Access Control Using Vehicle-Based Lidar

The subject disclosure relates to vehicles, and in particular to infrastructure access control using vehicle-based lidar.

Modern vehicles (e.g., a car, a motorcycle, a boat, or any other type of automobile) may be equipped with one or more cameras that provide back-up assistance, take images of the vehicle driver to determine driver drowsiness or attentiveness, provide images of the road as the vehicle is traveling for collision avoidance purposes, provide structure recognition (e.g., roadway signs, etc.), and/or the like, including combinations and/or multiples thereof. For example, a vehicle can be equipped with multiple cameras, and images from multiple cameras (referred to as “surround view cameras”) can be used to create a “surround” or “bird's eye” view of the vehicle. Some of the cameras (referred to as “long-range cameras”) can be used to capture long-range images (e.g., for object detection for collision avoidance, structure recognition, etc.).

Such vehicles can also be equipped with sensors such as a radar device(s), lidar device(s), and/or the like for perception tasks. Lidar (light detection and ranging) involves using light (e.g., a pulsed laser) to measure distance to objects by emitting laser pulses, detecting a reflection (e.g., off of an object) of the emitted laser pulse, and measuring the time between the emission and the detection. The measured time can be used to determine the distance between the lidar device and the detected object. Perception tasks can include one or more of object detection, classification, tracking, lane detection, road sign recognition, and obstacle avoidance. Perception tasks are particularly useful for an autonomous vehicle to provide the autonomous vehicle with real-time awareness of its environment to make safe and informed driving decisions. Images from the one or more cameras of the vehicle can also be used for detecting objects, tracking targets, and/or the like, including combinations and/or multiples thereof.

In one embodiment, a computer-implemented method for infrastructure access control using vehicle-based lidar is provided. The method includes detecting an infrastructure object in an environment in which a vehicle is operating based at least in part on lidar data collected by a lidar device of the vehicle. The method further includes emitting a custom lidar scan pattern associated with the infrastructure object, the custom lidar scan pattern being received by a receiver of the infrastructure object and causing the infrastructure object to implement an action. The method further includes, responsive to the infrastructure object implementing the action, autonomously controlling the vehicle to cause the vehicle to navigate with respect to the infrastructure object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the infrastructure object is an access gate, and wherein the action is opening the access gate.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include, subsequent to emitting the custom lidar scan pattern associated with the infrastructure object and prior to autonomously controlling the vehicle, determining whether the infrastructure object implemented the action successfully.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that autonomously controlling the vehicle is performed responsive to determining that the infrastructure object implemented the action successfully.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include, responsive to determining that the infrastructure object failed to implement the action successfully, initiating a remote system to acquire an image of the infrastructure object while the custom lidar scan pattern is emitted, receiving the image of the infrastructure object from the remote system, verifying a luminance change in the image of the infrastructure object matches an expected luminance change based on the custom lidar scan pattern with the infrastructure object, and responsive to verifying that the luminance change in the image of the infrastructure object matches the expected luminance change, causing, by the remote system, the infrastructure object to implement the action.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that emitting the custom lidar scan pattern associated with the infrastructure object includes: acquiring, by the lidar device, a point cloud of the infrastructure object, using a standard lidar scan pattern, normalizing the standard lidar scan pattern based on a location of the vehicle relative to a location of the infrastructure object, selecting the custom lidar scan pattern from a plurality of custom lidar scan patterns based on the infrastructure object, and causing the lidar device to emit the custom lidar scan pattern.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the custom lidar scan pattern causes the infrastructure object to implement the action responsive to the custom lidar scan pattern matching an expected custom lidar scan pattern.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the custom lidar scan pattern is defined by a custom frequency.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the method may include that the custom lidar scan pattern is defined by a custom sequence of lidar pulses.

In another embodiment, a vehicle is provided. The vehicle includes a driver monitoring system having a camera. The vehicle further includes a lidar device and a processing system for infrastructure access control using vehicle-based lidar. The processing system includes a memory having computer readable instructions and a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing system to perform operations. The operations include detecting an infrastructure object in an environment in which the vehicle is operating based at least in part on lidar data collected by the lidar device of the vehicle. The operations further include determining, using an image of an operator of the vehicle captured by the camera, whether the operator of the vehicle is an authorized operator of the vehicle. The operations further include, responsive to determining that the operator of the vehicle is the authorized operator of the vehicle, emitting a custom lidar scan pattern associated with the infrastructure object, the custom lidar scan pattern being received by a receiver of the infrastructure object and causing the infrastructure object to implement an action. The operations further include, responsive to the infrastructure object implementing the action, autonomously controlling the vehicle to cause the vehicle to navigate with respect to the infrastructure object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that the camera a first camera, the vehicle further comprising a second camera.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include a global positioning system (GPS) device, the operations further comprising determining a location of the infrastructure object based at least in part on information received from the GPS device.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that the custom lidar scan pattern associated with the infrastructure object is emitted based at least in part on the location of the infrastructure object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that the operations further include, responsive to determining that the operator of the vehicle is not the authorized operator of the vehicle, generating an alert.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that emitting the custom lidar scan pattern associated with the infrastructure object includes: acquiring, by the lidar device, a point cloud of the infrastructure object, using a standard lidar scan pattern, normalizing the standard lidar scan pattern based on a location of the vehicle relative to a location of the infrastructure object, selecting the custom lidar scan pattern from a plurality of custom lidar scan patterns based on the infrastructure object, and causing the lidar device to emit the custom lidar scan pattern.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that the operations further include, subsequent to emitting the custom lidar scan pattern associated with the infrastructure object and prior to autonomously controlling the vehicle, determining whether the infrastructure object implemented the action successfully.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that autonomously controlling the vehicle is performed responsive to determining that the infrastructure object implemented the action successfully.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the vehicle may include that the operations further include, responsive to determining that the infrastructure object failed to implement the action successfully, initiating a remote system to acquire an image of the infrastructure object while the custom lidar scan pattern is emitted, receiving the image of the infrastructure object from the remote system, verifying a luminance change in the image of the infrastructure object matches an expected luminance change based on the custom lidar scan pattern with the infrastructure object, and responsive to verifying that the luminance change in the image of the infrastructure object matches the expected luminance change, causing, by the remote system, the infrastructure object to implement the action.

In another embodiment a computer program product is provided. The computer program product includes a set of one or more computer-readable storage media and program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform computer operations for infrastructure access control using vehicle-based lidar. The computer operations include detecting an infrastructure object in an environment in which a vehicle is operating based at least in part on lidar data collected by a lidar device of the vehicle. The operations further include emitting a custom lidar scan pattern associated with the infrastructure object, the custom lidar scan pattern being received by a receiver of the infrastructure object and causing the infrastructure object to implement an action. The operations further include, responsive to the infrastructure object implementing the action, autonomously controlling the vehicle to cause the vehicle to navigate with respect to the infrastructure object.

In addition to one or more of the features described herein, or as an alternative, further embodiments of the computer program product may include that emitting the custom lidar scan pattern associated with the infrastructure object includes: acquiring, by the lidar device, a point cloud of the infrastructure object, using a standard lidar scan pattern, normalizing the standard lidar scan pattern based on a location of the vehicle relative to a location of the infrastructure object, selecting the custom lidar scan pattern from a plurality of custom lidar scan patterns based on the infrastructure object, and causing the lidar device to emit the custom lidar scan pattern.

The above features and advantages, and other features and advantages of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.

The following description is merely exemplary in nature and is not intended to limit the present disclosure, its application or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate like or corresponding parts and features. As used herein, the term module refers to processing circuitry that may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.

One or more embodiments described herein relates to infrastructure access control using vehicle-based lidar.

Infrastructure access control refers to the systems, apparatuses, and processes used to regulate and manage the entry and exit of vehicles or individuals to and from secure or restricted areas. This involves the use of various technologies and methods, such as identification verification, automated gates, and security protocols, to ensure that authorized entities can access specific infrastructure objects like toll roads, border crossings, gated communities, and secure facilities, while restricting access to unauthorized entities. The goal of infrastructure access control is to enhance security, improve operational efficiency, and provide real-time monitoring and management of access points.

Modern infrastructure access control systems often rely on physical security objects (referred to as “infrastructure objects”), such as gates, toll booths, and border crossings to regulate vehicle entry. These systems typically require drivers to interact with physical devices, such as RFID tags, badges, or manual input systems, to gain access. This interaction can be cumbersome and time-consuming, particularly for drivers who face challenges leaving their vehicles or for fleet operations that require efficient and secure access control.

Existing solutions for infrastructure access control present several disadvantages. Physical security elements often involve manual intervention, which can lead to delays and inefficiencies, especially in high-traffic areas. Additionally, reliance on physical devices, such as RFID tags or badges, introduces the risk of loss, theft, or damage, compromising security. Furthermore, these systems may not provide real-time monitoring and verification of driver identity, leading to potential unauthorized access and security breaches.

One or more embodiments described herein addresses these and other issues by utilizing advancements in lidar technology to enable real-time, customized scan patterns for infrastructure access control. One or more embodiments dynamically customizes and deploys custom lidar scan patterns. This approach enhances security and accessibility by allowing vehicles to be identified through customizable scan patterns observed by a receiver associated with an infrastructure object, such as a gate and or toll road. One or more embodiments provides continuous fleet monitoring and logistics management, ensuring secure and efficient access to infrastructure without requiring additional physical devices.

1 FIG. 100 102 104 100 100 100 100 100 shows a vehiclewith a processing systemand sensorsaccording to one or more embodiments. The vehiclecan be a car, a truck, a van, a bus, a motorcycle, a boat, or any other type of automobile. According to an embodiment, the vehicleis a hybrid electric vehicle, such as a plug-in hybrid electric vehicle (PHEV) partially or wholly powered by electrical power. According to another embodiment, the vehicleis an electric vehicle powered by electrical power. A battery is used to provide electrical power to components of the vehicle, such as an electric motor (not shown), electrical components (not shown), and/or the like, including combinations and/or multiples thereof. According to one or more embodiments, the vehicleis an autonomous or semi-autonomous vehicle. An autonomous vehicle is a vehicle that has self-driving capabilities. A semi-autonomous vehicle is a vehicle that has certain autonomous features (e.g., self-parking, lane keeping, etc.) but lacks full autonomous control.

102 104 104 104 102 104 102 100 104 The processing systemis located within the vehicle and is responsible for managing and processing data collected by the sensors. The sensorsare strategically positioned on the vehicle to gather various types of data from the vehicle's environment. The arrows between the sensorsand the processing systemindicate the flow of data from the sensorsto the processing system, highlighting the interaction between these components. This setup enables the vehicleto perform tasks related to infrastructure access control, such as detecting infrastructure objects and emitting custom lidar scan patterns. Examples of the sensorsinclude, but are not limited to, a lidar device, a camera device, a global positioning system (GPS) device, a driver monitoring system (DMS) camera device, and/or the like, including combinations and/or multiples thereof.

102 104 2 5 FIGS.-B Further features of the processing systemand the sensorsare now described with reference to.

2 FIG. 1 FIG. 6 FIG. 6 FIG. 102 202 204 210 212 102 102 100 102 102 600 600 Particularly,illustrates the processing system ofaccording to one or more embodiments. According to one or more embodiments, the processing systemincludes a processing device, a memory, a scan pattern engine, and an autonomous driving engine. It should be appreciated that the processing systemcan be any device suitable for performing or supporting infrastructure access control using vehicle-based lidar. For example, the processing systemcan be a device implemented in or otherwise associated with the vehicle, such as an electronic control unit (also referred to as an electronic control module). As another example, the processing systemcan be a smartphone, tablet computer, laptop computer, desktop computer, wearable computing device, and/or the like, including combinations and/or multiples thereof. As yet another example, the processing systemcan be the processing systemofand/or can include one or more components of the processing systemof.

202 102 202 202 102 202 621 6 FIG. The processing deviceis responsible for executing instructions and managing the overall operation of the processing system. The processing devicecan be any suitable processing circuitry for executing instructions and processing data. For example, the processing devicecan be a microcontroller, microprocessor, application-specific integrated circuit (ASIC), or any other type of processing unit capable of handling the computational demands of the processing system. The processing deviceis an example of one or more of the processing devicesof, as described in more detail herein.

204 214 102 204 214 204 204 622 623 624 6 FIG. The memorystores data (e.g., sensor data), computer-readable instructions, and algorithms useful for operation of the processing system. This may include real-time data processing, historical data analysis, and storage of firmware or software programs. The memoryis any suitable device for storing data, such as the sensor data, and/or instructions. For example, the memorycan be a combination of volatile memory (e.g., random access memory) and non-volatile memory (e.g., read-only memory, flash memory). The memoryis an example of one or more of the system memory, the random access memory, and/or the read-only memoryof, as described in more detail herein.

210 210 214 104 100 210 100 210 204 210 100 a 3 FIG. The scan pattern engineis a specialized component that generates and manages custom lidar scan patterns used for infrastructure access control. For example, the scan pattern engineprocesses sensor datafrom a lidar device (e.g., the lidar deviceof) to create a point cloud of the environment in which the vehicleis operating using a standard lidar scan pattern. The scan pattern enginethen normalizes the standard lidar scan pattern based on the location of the vehiclerelative to an infrastructure object (e.g., a gate). The scan pattern engineselects the appropriate custom lidar scan pattern from a plurality of predefined custom lidar scan patterns stored in the memory. These custom lidar scan patterns are tailored to specific infrastructure objects, such as particular gates or toll booths, and are designed to be recognized by receivers associated with those objects. By emitting these custom lidar scan patterns, the scan pattern engineensures that the infrastructure objects can accurately identify and respond to the vehicle, facilitating secure and efficient access control.

212 100 210 212 100 212 214 104 100 212 212 100 The autonomous driving enginecontrols the autonomous navigation capabilities of the vehicle, allowing the vehicle to navigate with respect to detected infrastructure objects. Once the scan pattern enginehas successfully emitted a custom lidar scan pattern and the infrastructure object has implemented a desired action (e.g., opening a gate), the autonomous driving enginecauses the vehicleto navigate with respect to the infrastructure object (e.g., drive through the open gate). The autonomous driving engineprocesses sensor datareceived from the sensors(e.g., the lidar device, camera devices, and GPS device) to determine the precise location and orientation of the vehicle. The autonomous driving enginethen generates control signals to steer, accelerate, or brake the vehicle as needed to safely and efficiently navigate through the opened gate or other infrastructure object. The autonomous driving engineensures that the vehiclecan autonomously perform complex maneuvers, reducing the need for manual intervention and enhancing the overall efficiency of the access control process.

2 FIG. 102 220 221 220 221 100 220 221 102 In the embodiment of, it is shown that the processing systemcan communicate with a remote systemand an infrastructure object system. The remote systemcan communicate with the vehicle's processing system to provide additional data or receive alerts, such as alerts to or from police or a fleet management system. The infrastructure object systemrepresents a system associated with an infrastructure object, such as a gate or toll booth, that interact with the vehicle. The dashed arrows indicate the flow of data and communication between these components, highlighting the interconnected nature of the system for effective infrastructure access control. The remote systemand the infrastructure object systemcan be any suitable computing system(s) for collecting data, analyzing data, storing data, communicating with other systems (such as the processing system), and/or the like, including combinations and/or multiples thereof.

3 FIG. 1 2 FIGS.and 6 FIG. 1 2 FIGS.and 300 300 300 102 600 300 illustrates a flow diagram of a methodfor infrastructure access control using vehicle-based lidar according to one or more embodiments. The methodcan be implemented using any suitable system or device. For example, the method, and its steps, can be implemented using the processing systemof, by the processing systemof, and/or the like, including combinations and/or multiples thereof. The methodis now described with reference tobut is not so limited.

100 104 100 104 104 104 104 104 104 102 300 302 104 304 104 306 308 300 306 302 304 308 300 312 102 100 100 310 104 a b c d a b c The vehiclecollects data using the sensors. For example, the vehiclecan be equipped with various sensors, such as a lidar device, a camera device, a GPS device, and a driver monitoring system (DMS) camera device. The sensorscollect data from the vehicle's environment and provide the data to the processing system. Particularly, the methodstarts at block, where the lidar deviceacquires a point cloud of the environment. At block, the camera deviceacquires an image of the environment. The data from these two sensors is used at blockto detect the presence of an infrastructure object, such as a gate. If no infrastructure object is detected (block, “No”), the methodreturns to blockto continue attempting to detect an infrastructure object using the data collected at blocksand. If an infrastructure object is detected (block, “Yes”), the methodmoves to block, where the processing systemchecks if the GPS location of the vehiclesubstantially matches a known location for a known infrastructure object. The GPS location of the vehicleis acquired at block, where the GPS deviceacquires the vehicle's location (e.g., GPS coordinates for the vehicle).

314 300 310 314 104 316 102 318 320 322 324 220 360 362 d If the location does not match (block, “No”), the methodreturns to blockto re-acquire the location. If the location matches (block, “Yes”), the DMS camera deviceacquires an image of the driver at block. The processing systemthen performs driver authentication at blockby verifying the image against pre-authorized identities stored at block. If the driver's identity is not confirmed (block, “No”), an alert is generated at block. The alert can be sent to a remote system, a fleet manager, and/or law enforcement.

322 210 104 326 104 328 104 104 100 a a a a If the driver's identity is confirmed (block, “Yes”), the scan pattern enginealerts the lidar deviceto begin scanning the environment at block. The lidar deviceacquires a point cloud of the infrastructure object using a standard lidar scan pattern at block. A standard lidar scan pattern refers to a predefined and consistent sequence of laser pulses emitted by the lidar deviceto map the surrounding environment. This pattern typically involves the lidar devicerotating or oscillating to cover a particular field of view, emitting laser pulses at regular intervals and angles. The returning laser pulses are measured to create a point cloud, which is a three-dimensional representation of the environment. The standard lidar scan pattern is designed to provide a comprehensive and uniform coverage of the area around the vehicle, allowing for the detection and identification of objects, obstacles, and infrastructure objects.

330 210 310 326 210 100 At block, the scan pattern enginenormalizes the standard lidar scan pattern based on a location of the vehicle (determined at block) relative to a location of the infrastructure object, which is known. For example, different vehicles may approach the same infrastructure object differently (e.g., a first vehicle may stop a distance from a gate while a second vehicle may stop a different distance from the gate relative to the first vehicle, the same vehicle may approach the gate from slightly different angles at different times, and/or the like, including combinations and/or multiples thereof). This variation causes differences in point clouds captured at block. Accordingly, the scan pattern enginenormalizes the standard lidar scan pattern by accounting for variations in location of the vehiclerelative to the infrastructure object.

332 210 100 At block, the scan pattern engineselects a custom lidar scan pattern from a plurality of custom patterns based on the detected infrastructure object. A custom lidar scan pattern refers to a tailored sequence of laser pulses emitted by a lidar device, which are specifically designed to identify the source of the custom lidar scan pattern. Unlike a standard lidar scan pattern, which provides uniform coverage of the environment, a custom scan pattern is optimized for identifying the source of the custom lidar scan pattern (e.g., the vehicle). This customization can involve altering the frequency, intensity, angle, or sequence of the laser pulses to create a unique pattern that can be recognized by a receiver on an infrastructure object, such as a gate or toll booth. According to one or more embodiments, the custom lidar scan pattern is selected from a plurality of predefined patterns stored in the system's memory, based on the type and characteristics of the detected infrastructure object. By using a custom lidar scan pattern, accurate identification and interaction with the infrastructure are provided, enabling actions, such as opening gates or granting access, thereby enhancing security and efficiency in access control processes.

334 210 104 336 336 324 336 338 340 a At block, the scan pattern enginetriggers the lidar deviceto emit the custom lidar scan pattern. It is then determined at block, such as by a system or device associated with the infrastructure object, whether the custom lidar scan pattern matches an expected lidar scan pattern associated with the infrastructure object. If the custom lidar scan patterns do not match (block, “No”), an alert is generated at block. If the custom lidar scan patterns matches (block, “Yes”), the infrastructure object is triggered to implement an action, such as opening a gate, at block. The action is logged at block.

342 102 342 212 100 344 342 221 221 346 350 348 348 350 100 At block, the processing systemchecks if the infrastructure action was completed successfully. If the action was successful (block, “Yes”), the autonomous driving enginecauses the vehicleto be autonomously controlled to navigate (e.g., to drive through the opened gate) at block. If the action was not successful (block, “No”), the infrastructure object systemis initiated to perform a secondary authentication using the custom lidar scan pattern. Particularly, the infrastructure object systemtriggers an infrastructure camera(s) at block, and an image is acquired using one or more infrastructure cameras associated with the infrastructure object at blockwhile the custom lidar scan pattern is emitted at block. For example, the LiDAR device emits a low voltage scan pattern at block, which represents the custom lidar scan pattern, and an infrastructure camera acquires image(s) at blockto capture the emitted custom lidar scan pattern from the vehicle.

352 211 354 324 354 356 358 212 100 344 At block, the infrastructure device systemverifies whether a luminance changes in the acquired image(s) match the expected changes based on the custom lidar scan pattern. If the luminance does not match (e.g., is not within a threshold amount of luminance) (block, “No”), an alert is generated at block. If the luminance matches (block, “Yes”), the infrastructure object is triggered to perform the action at block(e.g., to open the gate), and the entry is logged at block. The autonomous driving enginecauses the vehicleto be autonomously controlled to navigate (e.g., to drive through the opened gate) at block.

3 FIG. 3 FIG. 2 FIG. 6 FIG. 1 2 FIGS.and 6 FIG. 202 621 102 600 Additional processes also may be included, and it should be understood that the processes depicted inrepresent illustrations, and that other processes may be added, or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure. It should also be understood that the processes depicted inmay be implemented as programmatic instructions stored on a non-transitory computer-readable storage medium that, when executed by a processor (e.g., the processing deviceof, the processor(s)of, and/or the like, including combinations and/or multiples thereof) of a computing system (e.g., the processing systemof, the processing systemof, and/or the like, including combinations and/or multiples thereof), cause the processor to perform the processes described herein.

4 FIG. 1 2 FIGS.and 6 FIG. 1 3 FIGS.- 400 400 400 102 600 400 illustrates a flow diagram of a methodfor infrastructure access control using vehicle-based lidar according to one or more embodiments. The methodcan be implemented using any suitable system or device. For example, the method, and its steps, can be implemented using the processing systemof, by the processing systemof, and/or the like, including combinations and/or multiples thereof. The methodis now described with reference tobut is not so limited.

402 102 100 104 104 214 100 100 104 a a At block, the processing systemdetects an infrastructure object in the environment in which the vehicleis operating. This detection is based at least in part on data collected by the sensors. For example, the lidar devicecan collect lidar data (e.g., sensor data) about the environment in which the vehicleis operating, including the infrastructure object. The lidar device scans the environment surrounding the vehicleand identifies infrastructure objects that may be relevant for access control, such as gates, toll booths, or other infrastructure objects. The detection process involves analyzing point cloud data generated by the lidar deviceto recognize the presence and characteristics of the infrastructure object.

404 102 210 104 100 a At block, the processing system, using the scan pattern engineand the lidar device, emits a custom lidar scan pattern associated with the detected infrastructure object. This custom lidar scan pattern is specifically designed to be recognized by a receiver on the infrastructure object. According to one or more embodiments, the custom lidar scan pattern is unique to the vehicleand to the infrastructure object. The custom lidar scan pattern is selected from a plurality of predefined patterns based on the identified infrastructure object. The infrastructure device can be identified by location, type, and characteristics of the detected infrastructure object. When the receiver on the infrastructure object detects the custom lidar scan pattern, the infrastructure object is caused to implement a specific action, such as opening a gate or allowing access through a toll booth.

406 102 212 100 100 100 100 100 At block, responsive to the infrastructure object implementing the action, the processing system, using the autonomous driving engine, autonomously controls the vehicleto navigate with respect to the infrastructure object. This involves generating control signals to steer, accelerate, or brake the vehicleas appropriate to efficiently navigate the vehiclethrough the opened gate or other infrastructure object. The autonomous control ensures that the vehiclecan seamlessly and efficiently navigate the infrastructure object without manual intervention from the driver of the vehicleand/or other personnel, enhancing the overall efficiency and security of the access control process.

4 FIG. 4 FIG. 2 FIG. 6 FIG. 1 2 FIGS.and 6 FIG. 202 621 102 600 Additional processes also may be included, and it should be understood that the processes depicted inrepresent illustrations, and that other processes may be added, or existing processes may be removed, modified, or rearranged without departing from the scope of the present disclosure. It should also be understood that the processes depicted inmay be implemented as programmatic instructions stored on a non-transitory computer-readable storage medium that, when executed by a processor (e.g., the processing deviceof, the processor(s)of, and/or the like, including combinations and/or multiples thereof) of a computing system (e.g., the processing systemof, the processing systemof, and/or the like, including combinations and/or multiples thereof), cause the processor to perform the processes described herein.

5 FIG.A 5 FIG.B 5 FIG.B 5 FIG.A 5 FIG.B 500 104 510 104 501 502 503 504 505 506 507 508 509 104 500 501 509 104 104 501 502 503 509 510 104 500 104 501 507 104 500 510 a a a a a a a a depicts a block diagram of a standard lidar scan patternfor a lidar device (e.g., the lidar device) according to one or more embodiments.depicts a block diagram of a custom lidar scan patternfor a lidar device (e.g., the lidar device) according to one or more embodiments. In these examples, each point,,,,,,,,represents a point that is captured as part of a lidar scan by the lidar device. For the standard lidar scan pattern, the scan is performed using a predefined and consistent sequence of laser pulses (represented by the points-) emitted by the lidar deviceto map the surrounding environment. For example, the lidar devicemay first emit a laser point at point, then at point, then at point, and so forth continuing until point. In such an example, each of the pulses may be performed using the same parameters (e.g., substantially the same amount of time, at substantially the same frequency, and/or the like, including combinations and/or multiples thereof). However, in the case of the custom lidar scan pattern, the sequence and properties of the scan performed by the lidar devicediffers from the standard lidar scan pattern. For example, in, the lidar devicescans the points-in the order shown. It should further be appreciated that the lidar devicecan vary properties of the lidar scan at each point. For example, the amount of time of laser pulses, the frequency of the laser pulses, and/or the like, including combinations and/or multiples thereof, can be varied from point-to-point. Given the extensive number of points performed in a lidar scan (e.g., hundreds of thousands or even millions of points), and the opportunity to vary parameters with each point, the number of possibilities of custom lidar scan patterns is vast and may enable custom scan patterns to be unique (e.g., unique to a user, a vehicle, and an infrastructure object). It should be appreciated that the standard lidar scan pattern() and the custom lidar scan pattern() are merely simplified examples and that many different arrangements of standard and custom lidar scan patterns are possible.

6 FIG. 600 600 600 621 621 621 621 621 621 621 622 633 622 623 624 633 600 a b c It is understood that one or more embodiments described herein is capable of being implemented in conjunction with any other type of computing environment now known or later developed. For example,depicts a block diagram of a processing systemfor implementing the techniques described herein. In accordance with one or more embodiments described herein, the processing systemis an example of a cloud computing node of a cloud computing environment. In examples, processing systemhas one or more central processing units (referred to also as “processors” or “processing resources” or “processing devices”),,, etc. (collectively or generically referred to as processor(s)and/or as processing device(s)). In aspects of the present disclosure, each processorcan include a reduced instruction set computer (RISC) microprocessor. Processorsare coupled to a system memoryand/or various other components via a system bus. The system memorycan include one or more temporary and/or persistent memory devices, such as a random access memory (RAM), a read-only memory (ROM), and/or the like, including combinations and/or multiples thereof. The system busmay include a basic input/output system (BIOS), which controls certain basic functions of processing system.

627 626 633 627 635 636 627 635 636 634 640 600 634 626 633 638 600 Further depicted are an input/output (I/O) adapterand a network adaptercoupled to system bus. I/O adaptermay be a small computer system interface (SCSI) adapter that communicates with a hard diskand/or a storage deviceor any other similar component. I/O adapter, hard disk, and storage deviceare collectively referred to herein as mass storage. Operating systemfor execution on processing systemmay be stored in mass storage. The network adapterinterconnects system buswith an outside networkenabling processing systemto communicate with other such systems.

639 633 632 626 627 632 633 633 628 632 629 630 631 633 628 A display (e.g., a display monitor)is connected to system busby display adapter, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller. In one aspect of the present disclosure, adapters,, and/ormay be connected to one or more I/O buses that are connected to system busvia an intermediate bus bridge (not shown). Suitable I/O buses for connecting peripheral devices such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI). Additional input/output devices are shown as connected to system busvia user interface adapterand display adapter. A keyboard, mouse, and speakermay be interconnected to system busvia user interface adapter, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.

600 637 637 637 In some aspects of the present disclosure, processing systemincludes a graphics processing unit (GPU). Graphics processing unitis a specialized electronic circuit designed to manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display. In general, graphics processing unitis very efficient at manipulating computer graphics and image processing and has a highly parallel structure that makes it more effective than general-purpose CPUs for algorithms where processing of large blocks of data is done in parallel.

600 621 622 634 625 630 631 639 622 634 640 600 Thus, as configured herein, processing systemincludes processing capability in the form of processors, storage capability including the system memoryand mass storage, input means such as keyboardand mouse, and output capability including speakerand display. In some aspects of the present disclosure, a portion of system memoryand mass storagecollectively store the operating systemto coordinate the functions of the various components shown in processing system.

One or more embodiments offer several significant benefits and advantages over existing approaches to infrastructure access control, including (but not limited to) the following.

Secure entry is granted to vehicles through customized lidar scan patterns, eliminating the need for additional hardware, such as RFID transmitters or badges. This reduces the risk of loss, theft, or damage to physical security devices.

One or more embodiments provide robust protection against unauthorized entry to secure locations by verifying the driver's identity using existing vehicle hardware, such as a DMS. This ensures that only authorized drivers can access secure areas.

One or more embodiments mitigate subjective decisions by security personnel and reduce gate congestion by automating the access control process. This leads to faster and more efficient entry for authorized vehicles.

One or more embodiments enable Vehicle-to-Infrastructure (V2X) logistics management through continuous monitoring of infrastructure object, such as gates, booths, and infrastructure objects, along a route. This ensures real-time tracking and management of vehicles, including fleet operations for example.

One or more embodiments increase accessibility for users who require additional assistance when encountering an infrastructure object, such as a gate. By automating the access control process, the need for drivers to exit their vehicles to interact with physical security elements is reduced.

One or more embodiments dynamically customize and deploy lidar scan patterns based on the specific infrastructure object, enhancing the flexibility and adaptability of the access control process.

Once access is granted, one or more embodiments can autonomously control the vehicle to navigate with respect to the infrastructure object, further streamlining the entry process and reducing the need for manual intervention.

In case of unauthorized access attempts or failures in the access control process, one or more embodiments can generate real-time alerts and report to third parties, such as remote systems, fleet managers, or law enforcement, ensuring prompt response to security breaches.

These and other benefits may be possible in various embodiments as described herein.

The terms “a” and “an” do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item. The term “or” means “and/or” unless clearly indicated otherwise by context. Reference throughout the specification to “an aspect”, means that a particular element (e.g., feature, structure, step, or characteristic) described in connection with the aspect is included in at least one aspect described herein, and may or may not be present in other aspects. In addition, it is to be understood that the described elements may be combined in any suitable manner in the various aspects.

When an element such as a layer, film, region, or substrate is referred to as being “on” another element, it can be directly on the other element or intervening elements may also be present. In contrast, when an element is referred to as being “directly on” another element, there are no intervening elements present.

Unless specified to the contrary herein, all test standards are the most recent standard in effect as of the filing date of this application, or, if priority is claimed, the filing date of the earliest priority application in which the test standard appears.

Unless defined otherwise, technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in the art to which this disclosure belongs.

While the above disclosure has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from its scope. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the disclosure without departing from the essential scope thereof. Therefore, it is intended that the present disclosure not be limited to the particular embodiments disclosed, but will include all embodiments falling within the scope thereof.